DEMAU: Decompose, Explore, Model and Analyse Uncertainties

TL;DR

DEMAU is an open-source tool designed to visualize and analyze different types of uncertainties in machine learning classification models, aiding in understanding and improving model performance.

Contribution

It introduces a simple, educational tool that decomposes and visualizes total, epistemic, and aleatoric uncertainties in classification models.

Findings

Facilitates understanding of uncertainty components in models

Supports active and adaptive learning strategies

Enhances interpretability of model predictions

Abstract

Recent research in machine learning has given rise to a flourishing literature on the quantification and decomposition of model uncertainty. This information can be very useful during interactions with the learner, such as in active learning or adaptive learning, and especially in uncertainty sampling. To allow a simple representation of these total, epistemic (reducible) and aleatoric (irreducible) uncertainties, we offer DEMAU, an open-source educational, exploratory and analytical tool allowing to visualize and explore several types of uncertainty for classification models in machine learning.